Dynamic productivity content rendering based upon user interaction patterns

ABSTRACT

An efficient blend of home/personal and work/productivity related content based on a user&#39;s intent is provided, wherein the user&#39;s intent can be determined based on context information, learned user interaction patterns, and historical work and home characteristics and patterns. The system is individualized to the user and operative to generate a user experience that provides a blend of relevant home/personal and work/productivity related information to the user based on the user&#39;s current work and life characteristics. From determined user intent, various aspects provide personalized computing experiences tailored to the user and, in some examples, incorporation of the user&#39;s patterns into an efficient blend of personal and productivity workflows. In further examples, the blend of home/personal and work/productivity related content and workflows are selectively displayed to the user such that screen resources are efficiently and advantageously allocated based on a determined relevance to the user&#39;s current work and life characteristics.

BACKGROUND

Due in part to an ability to keep up with work related tasks through theevolution of technology, people's personal lives are more and moreblended with work activities. Likewise, due in part to the prevalence ofmobile computing devices, people's professional or work lives are moreand more blended with personal or home activities. In some cases, workmay encroach into a person's personal life more than might be wanted.Accordingly, the balance between work life and home life may beunbalanced/unhealthy for the user's personal and/or family life dynamic.While efforts have been made to increase user satisfaction throughpersonalization of device and/or service operation, efforts have notbeen made to deliver a user experience that maximizes the work-lifebalance of a person's life.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription section. This summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended as an aid in determining the scope of the claimed subjectmatter.

Aspects are directed to an automated system, method, and computerreadable storage device for determining an efficient blend ofhome/personal and work/productivity related content based on a user'sintent. For example, the user's intent can be determined based oncontext information, learned user interaction patterns, and historicalwork and home characteristics and patterns. The system is individualizedto the user, and is further operative or configured to generate a userexperience that provides a blend of relevant home/personal andwork/productivity related information to the user based on the user'scurrent work and life characteristics. According to examples, the userinteraction patterns and historic work and home characteristics andpatterns can be used to infer user intent. From an inferred user intent,various aspects provide personalized computing experiences tailored tothe user and, in some examples, incorporation of the user's patternsinto an efficient blend of personal and productivity workflows. Infurther examples, the blend of home/personal and work/productivityrelated content and workflows are selectively displayed to the user suchthat screen resources are efficiently and advantageously allocated basedon a determined relevance to the user's current work and lifecharacteristics.

Examples are implemented as a computer process, a computing system, oras an article of manufacture such as a device, computer program product,or computer readable medium. According to an aspect, the computerprogram product is a computer storage medium readable by a computersystem and encoding a computer program of instructions for executing acomputer process.

The details of one or more aspects are set forth in the accompanyingdrawings and description below. Other features and advantages will beapparent from a reading of the following detailed description and areview of the associated drawings. It is to be understood that thefollowing detailed description is explanatory only and is notrestrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various aspects. In the drawings:

FIG. 1 is a block diagram showing an example operating environment forimplementation of the present disclosure;

FIG. 2 is a block diagram showing an example computing architecture forimplementing aspects of the present disclosure;

FIG. 3 is an illustration of an example user interface display showingan example user interface arranged wherein the arrangement ofinformation in the user interface is personalized to a particular user;

FIGS. 4A-B are a flow chart showing general stages involved in anexample method for generating a user experience that provides a blendedworkflow of relevant information to the user based on the user's currentwork and life characteristics;

FIG. 5 is a block diagram illustrating example physical components of acomputing device;

FIGS. 6A and 6B are simplified block diagrams of a mobile computingdevice; and

FIG. 7 is a simplified block diagram of a distributed computing system.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description refers to the same or similar elements.While examples may be described, modifications, adaptations, and otherimplementations are possible. For example, substitutions, additions, ormodifications may be made to the elements illustrated in the drawings,and the methods described herein may be modified by substituting,reordering, or adding stages to the disclosed methods. Accordingly, thefollowing detailed description is not limiting, but instead, the properscope is defined by the appended claims. Examples may take the form of ahardware implementation, or an entirely software implementation, or animplementation combining software and hardware aspects. The followingdetailed description is, therefore, not to be taken in a limiting sense.

Aspects of the present disclosure are directed to a method, system, andcomputer readable storage device for generating a user experience thatprovides a blended workflow of relevant information to the user based onthe user's current work and life characteristics. Aspects of the presentdisclosure relate to inferring a current mode or context associated witha user based on user interactions with one or more computing devices,and providing a relevant blend of content to the user based on theinferred context. In various examples, data corresponding to useractivity and other context information can be gathered over time usingsensors on one or more user devices associated with the user. From thishistorical user activity information, a computer system learns userinteraction patterns associated with home/personal contexts andwork/productivity contexts. Based on determined patterns of userinteractions, predictions of user priorities can be determined and usedto provide improved user experiences. Examples of these improved userexperiences, which are further described below, include personalizationservices, such as tailoring content for the user and arranging thecontent in an improved display which results in more efficient userinteraction and improved user efficiency.

Advantageously, the disclosed aspects enable the benefit of technicaleffects that include, but are not limited to, increased computationalefficiency and reductions in bandwidth while providing an improved userexperience by providing a blended workflow of relevant information tothe user. For example, using aspects of the present disclosure, apersonal digital assistant, a web search engine, or other application isenabled to retrieve information or services that better satisfyindividual users' interests, preferences, and needs, thus reducingadditional processing and bandwidth usage associated with avoidablesearches for information that may not satisfy users. Users are therebyprovided tools that produce an efficient blend of personal andwork/productivity related information and that deliver an experience tousers that maximizes the work-life balance of a person's life.

With reference now to FIG. 1, a block diagram is provided showing anexample operating environment 100 in which aspects of the presentdisclosure can be employed. It should be understood that this and otherarrangements described herein are provided as examples. Otherarrangements and elements can be used in addition to or instead of thoseshown in FIG. 1. Various functions described herein as being performedby one or more elements or components can be carried out by hardware,firmware, and/or software. For example, some functions can be carriedout by a processor executing instructions stored in memory. Asillustrated, the example operating environment 100 includes one or moreclient computing devices 102 a-n (generally 102), a number of datasources 104 a-n (generally 104), at least one server 106, sensors 108,and network 110. Each of the components illustrated in FIG. 1 can beimplemented via any type of computing device, such as the computingdevices described in reference to FIGS. 5, 6A, 6B, and 7. As an example,the one or more client computing devices 102 can be one of various typesof computing devices, such as tablet computing devices, desktopcomputers, mobile communication devices, laptop computers, laptop/tablethybrid computing devices, large screen multi-touch displays, vehiclecomputing systems, gaming devices, smart televisions, wearable devices,internet of things (IoT) devices, etc.

The components can communicate with each other via network 110, whichcan include, without limitation, one or more local area networks (LANs)or wide area networks (WANs). In some examples, network 110 comprisesthe Internet and/or a cellular network, amongst any of a variety ofpossible public or private networks. As should be appreciated, anynumber of client computing devices 102, data sources 104, and servers106 can be employed within the example operating environment 100 withinthe scope of the present disclosure. Each can comprise a single deviceor a plurality of devices cooperating in a distributed environment. Forexample, the server 106 can be provided via multiple devices arranged ina distributed environment that collectively provide variousfunctionalities described herein. In some examples, other components notshown can be included within the distributed operating environment 100.

According to an aspect, the one or more data sources 104 can comprisedata sources or data systems that are configured to make data availableto any of the various components of operating environment 100 or of theexample system 200 described below with reference to FIG. 2. In someexamples, the one or more data sources 104 are discrete from the one ormore client computing devices 102 and the at least one server 106. Inother examples, the one or more data sources 104 are incorporated orintegrated into at least one of the client computing devices 102 orservers 106.

The example operating environment 100 can be used to implement one ormore of the components of the example system 200 described in FIG. 2including components for collecting user data, monitoring interactionevents and characteristics, understanding user interaction patterns,work/productivity patterns, and home/personal patterns, consuming userinteraction pattern information for determining an efficient blend ofhome/personal information and work/productivity information based on adetermined threshold according to a user's current work and lifecharacteristics to provide an improved user experience, and to generatea personalized display of content based on a user's intent. Referringnow to FIG. 2, a block diagram is provided showing aspects of an examplecomputing system architecture suitable for implementing various aspectsof the present disclosure. The system 200 represents only one example ofa suitable computing system architecture. Other arrangements andelements can be used in addition to or instead of the elements shown. Asshould be appreciated, elements described herein are functional entitiesthat can be implemented as discrete or distributed components, or inconjunction with other components, and in any suitable combination orlocation.

As illustrated, the example system 200 includes a user data collector202, a user interaction monitoring engine 204, a pattern understandingengine 206, a context threshold engine 220, at least one user data store208, at least one blended content client 210, and, in some examples, apersonalized display engine 218. In one example, functions performed bycomponents of the example system 200 are associated with one or moreapplications 212, services 216, or a digital personal assistant 214. Thecomponents of the example system 200 can operate on one or more clientcomputing devices 102, servers 106, can be distributed across one ormore client computing devices 102 and servers 106, or can be implementedin the cloud. In some examples, one or more of the components of theexample system 200 are distributed across network 110.

According to an aspect, the user data collector 202 is operative orconfigured to receive or collect user data from one or more data sources104, and to store the received or collected user data in one or moredata stores, such as user data store 208, where it can be available toother components of the system 200. In some examples, the user data arestored in or associated with a user profile. According to examples, theuser data can include data received from a variety of data sources 104where the data may be available in a variety of formats. According toone aspect, a data source 104 can include a cloud-based knowledge base.According to another aspect, a data source 104 can include a relationalknowledge graph illustrative of a repository of entities andrelationships between entities. In a knowledge graph, entities arerepresented as nodes, and attributes and relationships between entitiesare represented as edges connecting the nodes. Thus, a knowledge graphprovides a structured schematic of entities and their relationships toother entities. According to examples, edges between nodes can representan inferred relationship or an explicit relationship. According to anaspect, a knowledge graph can be continually updated with content minedfrom a plurality of other data sources 104 (e.g., web pages or othernetworked data stores). In some examples, the user data received by theuser data collector 202 are collected by one or more sensors 108integrated with or communicatively attached to one or more computingdevices, such as one or more client computing devices 102, servers 106,or data sources 104. The one or more sensors 108 can be embodied ashardware, software, or a combination of hardware and software operativeor configured to sense, detect, or otherwise obtain user data.

By way of example and not limitation, user data can include data thatare sensed or determined from the one or more sensors 108, such aslocation information of a client computing device 102, properties orcharacteristics of the computing device(s) (such as device state,charging data, date/time, or other information derived from thecomputing device) and user interaction information (e.g., applicationusage; online activity; searches; voice data such as automatic speechrecognition; activity logs; communications data including calls, texts,instant messages, and emails; website posts; and other user dataassociated with communication events). In some examples, userinteraction information includes information associated with userinteractions that occur over more than one computing device. Userinteraction information can further include user histories, sessionlogs, application data, contacts data, calendar and schedule data,notification data, social-network data, news (including popular ortrending items on search engines or social networks), online gamingdata, ecommerce activities, user-account(s) data (which can include datafrom user preferences or settings associated with apersonalization-related application 212, a digital personal assistant214, or service 216), home-sensor data, appliance data, globalpositioning system (GPS) data, vehicle signal data, traffic data,weather data, wearable device data, other user device data (which mayinclude device settings, profiles, network-related information (e.g.,network name or ID, domain information, workgroup information,connection data, wireless network data, or configuration data, dataregarding the model number, firmware, or equipment, device pairings, orother network-related information), gyroscope data, accelerometer data,payment or credit card usage data, purchase history data, or othersensor data that may be sensed or otherwise detected by a sensor 108 (orother detector) component(s). For example, other sensor data can includedata derived from a sensor component associated with the user (includinglocation, motion, orientation, position, user-access, user interactions,network-access, user device charging, or other data that is capable ofbeing provided by one or more sensors), and other sources of data thatcan be sensed or determined as described herein.

With reference still to FIG. 2, the user interaction monitoring engine204 is operative or configured to monitor received user data forinformation that can be used to determine user interaction information.In some examples, determining user interaction information includesidentifying and tracking characteristics or other information associatedwith specific user interactions and related context information. In someexamples, aspects of the user interaction monitoring engine 204 areoperative or configured to determine user interactions associated with aparticular user based on the monitored user data. For example,characteristics or information associated with specific userinteractions can include home/personal related characteristics orwork/productivity related characteristics. An example of a userinteraction can include a user reading a document, and extracted userinteraction related characteristics can include information such as anapplication 212 used to perform the interaction (e.g., access thedocument), document data, document metadata, the user's interactionswith the document, as well as context information, such as the type ofclient computing device 102 being used to access the document, theuser's location when accessing the document, information about thelocation (e.g., office location, home location, school, restaurant, gym,vacation spot, etc.), other entities associated with the interaction,temporal data (e.g., time of day, day of the week, month of the year,usage duration), other documents that the user interacts with, anemotional state of the user determined via an emotional system, theuser's activity when accessing the document (e.g., traveling, walking,waiting, exercising), other applications 212 being used by the userconcurrent to the user interaction, the user's activity preceding orfollowing the user interaction, the weather, whether the user's clientcomputing device 102 is paired with another device (e.g., a speaker, adisplay), etc.

The user interaction information determined by the user interactionmonitoring engine 204 can include user interaction information frommultiple computing devices associated with the user and/or fromcloud-based services associated with the user (such as email, calendars,social media, or similar information sources), and which can includecontext information associated with the identified user interactions. Insome examples, the user interaction monitoring engine 204 is operativeor configured to determine current or near-real-time user interactioninformation. In other examples, the user interaction monitoring engine204 is operative or configured to determine historical user interactioninformation, which can be determined based on gathering observations ofuser interactions over time, accessing user logs of past interactions(e.g., applications launched or accessed, files accessed, modified,copied, etc., websites navigated to, online content downloaded andrendered or played, or similar user interactions). In some examples, aseries or sequence of user device interactions can be mapped to aninteraction event, such that the interaction event is detected upondetermining that the user data indicates the series or sequence of userinteractions has been carried out by the user.

The extracted user interaction information can be provided to othercomponents of the example system 200, such as the pattern understandingengine 206, the context threshold engine 220, the blended content client210, or the personalized display engine 218. Further, the extracted userinteraction information can be stored in a user profile associated withthe user. With reference still to FIG. 2, the pattern understandingengine 206 is operative or configured to derive user interactionpatterns based on user interaction information determined by the userinteraction monitoring engine 204. In some examples, the patternunderstanding engine 206 runs on a server 106, as a distributedapplication across multiple devices, or in the cloud. According to anaspect, one or more algorithms can be applied to the user interactioninformation to determine or understand a set of patterns that can becharacterized as likely home/personal related or work/productivityrelated. For example, home/personal related or work/productivity relatedpatterns can be determined based on similar instances of observation ofuser interactions or associated context information.

In some examples, the pattern understanding engine 206 is operative orconfigured to determine semantic information associated with userinteraction related characteristics identified by the user interactionmonitoring engine 204. For example, while a particular user interactionfeature can indicate a specific document read by the user, a semanticanalysis performed by the pattern understanding engine 206 can determinea category of the document, related documents, themes or topics or otherentities associated with the document, or user interactions. In someexamples, the pattern understanding engine 206 is operative orconfigured to determine additional user interaction relatedcharacteristics semantically related to the user interaction, which canbe used for identifying user interaction patterns. According to anaspect, inferred interaction pattern information is provided to thecontext threshold engine 220. In some examples, a correspondingconfidence score is calculated for the identified likely home/personalrelated or work/productivity related characteristics or patterns.

According to an aspect, the pattern understanding engine 206 can usesemantic analysis to identify home/personal related or work/productivityrelated characteristics and determine other relevant features ofinteraction events to determine patterns. For example, in addition todetermining, for a particular user, particular interactions (e.g., tasksthat the user typically performs at certain times, particular websitesthat the user visits at certain times, particular communications theuser has at certain times), semantic analysis can determine additionalcharacteristics and features of the interactions, such as categories ofthe tasks (e.g., family-related task, scheduling-related task,hobby-related task, work project-related task), categories of thewebsites (e.g., sports-related website, news-related website, celebritygossip-related website, politics-related website), and communications.Further, using semantic analysis, the pattern understanding engine 206is operative or configured to categorize the context of particularinteractions as home/personal related or work/productivity related.

In some examples, context of a particular interaction can be categorizedas home/personal related based on various characteristics of theinteraction although the home/personal related interaction is beingperformed during working hours at a location that is known aswork-related (e.g., at the user's office). For example, a pattern ofvisiting shopping-related websites at work over lunch can be determinedwhere a user routinely visits shopping-related websites at work overlunch. Likewise, the context of a particular interaction can becategorized as work/productivity related based on variouscharacteristics of the interaction although the work/productivityrelated interaction is being performed at a time outside of workinghours at the user's home. For example, a pattern of schedulingwork-related meetings from home late at night can be determined wherethe user routinely schedules work-related meetings from home.

With reference still to FIG. 2 and according to an aspect, the contextthreshold engine 220 is operative or configured to determine anefficient blend of home/personal and work/productivity related contentor information based on the user's intent and an appropriate contextinferred from the user interaction patterns and historic home/personalrelated or work/productivity related characteristics, and currenthome/personal related or work/productivity related characteristics. Insome examples, the context threshold engine 220 determines a ratio ofhome/personal information to work/productivity related information tosurface to a particular user with respect to the user's current contextand based on learned patterns and characteristics of the user's pastinteractions. According to an aspect, a user can interact with a clientcomputing device 102. In response to receiving an indication of the userinteraction, the pattern understanding engine 206 can use semanticanalysis to identify home/personal related or work/productivity relatedcharacteristics and determine semantic information related to the userinteraction for inferring interactions that the user is likely toperform or information that is likely to be relevant to the user basedon historical user interactions that are similar to the currentinteraction. According to an aspect, the inferred interactions caninclude home/personal interactions and work/productivity interactions.

According to an aspect, the context threshold engine 220 determines ahome/personal-work/productivity context threshold based on the inferreduser intent and context information associated with user's currentcontext. For example, based on various characteristics associated withthe current user interaction and on a pattern determined to be the mostsimilar to the current interaction by the pattern understanding engine206, the context threshold engine 220 determines ahome/personal-work/productivity context threshold. In one example, thehome/personal-work/productivity context threshold is used to calculateedge weights (in the knowledge graph) representing relationships betweenthe user and entities associated with the inferred interactions that theuser is likely to perform or information likely to be relevant to theuser in the current context. According to an example, two particularnodes can have a strong edge weight between them at one particularhome/personal-work/productivity context threshold value, thus indicatingan interactivity, content type, or other information item with a highdegree of relevance to the user in a particular context. Alternatively,in a different context, the two particular nodes can have a lesser edgeweight between them based on the calculatedhome/personal-work/productivity context threshold value, thus indicatingan interactivity, content type, or other information item with a lowdegree of relevance to the user in the particular context.

Consider, for example, that a user is at home on a weekday night (e.g.,as identified by the user interaction monitoring engine 204) and thatthe user typically works from home for a couple of hours on weekdaynights (e.g., based on a pattern understood by the pattern understandingengine 206). Additional context information can be collected (e.g., viavarious sensors 108) and analyzed that indicates that the user iscurrently in an emotional state of distress and that the user is likelyexperiencing a stressful life event based on information collected fromvarious data sources 104, such as the user's calendar entries,communications, and recent browsing history. Accordingly, based oncurrent home/personal related or work/productivity relatedcharacteristics and the users' current state as inferred according touser interaction data, user data, and context information, such ascontext information collected from one or more sensors 108, the contextthreshold engine 220 can determine current likely priorities of theuser, identify whether those priorities are home/personal related orwork/productivity related, and to determine an efficient blend or ratioof home/personal related and work/productivity related information tosurface to the user. In various examples, home/personal related andwork/productivity related information can include documents, webcontent, specific application-related content (e.g., calendarinformation, electronic messages), contact information, etc. In someexamples, the home/personal and work/productivity related informationincludes a blended workflow comprised of home/personal related andwork/productivity related tasks.

According to an aspect, the context threshold engine 220 provides thedetermined home/personal-work/productivity context threshold to ablended content client 210, such as an application 212, a digitalpersonal assistant 214, or a services 216 operative or configured toprovide personalized content to the user based on the user's explicitlydefined or inferred intent. In some examples, one or more components ofthe system 200 (e.g., the user data collector 202, the user interactionmonitoring engine 204, the pattern understanding engine 206, the contextthreshold engine 220, the user data store 208, or the personalizeddisplay engine 218) are exposed to one or more applications 212, digitalpersonal assistants 214, or services 216 as an API (ApplicationProgramming Interface).

A user can use an application 212 on a client computing device 102 for avariety of tasks, which can include, for example, to write, calculate,draw, take and organize notes, organize and prepare presentations,search for and obtain information, send and receive electronic mail,make music, and the like. Examples of suitable applications 212 include,but are not limited to, word processing applications, spreadsheetapplications, slide presentation applications, electronic mailapplications, drawing applications, note-taking applications, webbrowser applications, game applications, and mobile applications.Applications 212 can include thick client applications, which are storedlocally on the client computing device 102, or may include thin clientapplications (i.e., web applications) that reside on a remote server 106and accessible over a network 110 or a combination of networks. A thinclient application can be hosted in a browser-controlled environment orcoded in a browser-supported language and can rely on a common webbrowser to render the application executable on the client computingdevice 102. According to examples, the application 212 is a program thatis launched and manipulated by an operating system, and manages content,which can be displayed on a display screen. In some examples, anapplication 212 is operative or configured to generate and provide agraphical user interface (GUI) that allows a user to interact withapplication functionality and electronic content.

Digital personal assistant functionality can be provided as or by astand-alone digital personal assistant application, part of anapplication 212, or part of an operating system of the client computingdevice 102. In some examples, the digital personal assistant 214 employsa natural language user interface (UI) that can receive spokenutterances from a user that are processed with voice or speechrecognition technology. For example, the natural language UI can includean internal or external microphone, camera, and various other types ofsensors 108. The digital personal assistant 214 can support variousfunctions, which can include interacting with the user (e.g., throughthe natural language UI or GUIs); performing tasks (e.g., making note ofappointments in the user's calendar, sending messages and emails, etc.);providing services (e.g., answering questions from the user, mappingdirections to a destination, other application 212 or service 216functionalities that supported by the digital personal assistant 214,etc.); gathering information (e.g., finding information requested by theuser about a book or movie, locating the nearest Italian restaurant,etc.); operating the client computing device 102 (e.g., settingpreferences, adjusting screen brightness, turning wireless connectionson and off); and various other functions. The functions listed above arenot intended to be exhaustive and other functions may be provided by thedigital personal assistant 214. In examples, the applications 212 ordigital personal assistant 214 receive input from the user via variousinput methods, such as those relying on mice, keyboards, and remotecontrols, as well as Natural User Interface (NUI) methods, which enablea user to interact with a device in a “natural” manner, such as viaspeech recognition, touch and stylus recognition, gesture recognitionboth on screen and adjacent to the screen, air gestures, head and eyetracking, voice and speech, vision, touch, hover, gestures, and machineintelligence.

In some examples, the blended content client 210 includes web searchfunctionality, for example, to locate, retrieve and display contentavailable on the World Wide Web, including, for example, Web pages,images, videos, and the like. The user can use the blended contentclient 210 to surface information based on a user query. For example,the user query is an example of a current user interaction. Based on theuser interaction, context information, learned user interactionpatterns, and historical home/personal and work/productivity relatedcharacteristics and patterns, the user intent and current likelypriorities of the user are inferred for determining an efficient blendor ratio of home/personal related and work/productivity relatedinformation to surface to the user. According to an aspect, based on theblend of home/personal related and work/productivity related informationdetermined based on a calculated home/personal-work/productivity contextthreshold value, results for the user query are ranked. For example, thecurrent user interaction can be inferred to be home/personal related,and accordingly, query results that are categorized as home/personalrelated can be ranked higher and surfaced to the user with higherpriority than results that are categorized as work/productivity related.In some examples, the amounts of home/personal related information andwork/productivity related information surfaced to the user aredetermined from a ratio based on the home/personal-work/productivitycontext threshold. For example, based on various pieces of contextinformation, a determination can be made that a user is on vacation.Accordingly, a web search initiated by the user while on vacation, cansurface results that are less-work/productivity related and morehome/personal related. For example, results for a “screwdrivervarieties” web query while on vacation can surface more drink-relatedresults, while results for a “screwdriver varieties” web query while ina work context can surface more tool-related results.

According to an aspect, a personalized display engine 218 is integratedwith or communicatively attached to a blended content client 210. Insome examples, the personalized display engine 218 is operative orconfigured to selectively allocate screen resources for the blend ofhome/personal and work/productivity related information based on adetermined priority or relevance of an information item to the user'scurrent context, wherein the relevance is calculated based on thedetermined home/personal-work/productivity context threshold value. Forexample, a GUI generated for an application 212, digital personalassistant 214, or service 216 can be arranged such that information thatis determined to be more relevant to the user is positioned in the GUIat a place of prominence, that is, in a place that is easily seen and iseasily accessible to the user. In some examples, the size of the displayof priority information is larger than the size of the display of lessrelevant information. Various examples will be described below withreference to FIG. 3.

With reference now to FIG. 3, an example GUI 300 display is shown,wherein the arrangement of information in the GUI 300 is personalized toa particular user as determined by the personalized display engine 218.In a first example, consider a user whose current context indicates ascenario where the user is at work. According to user data, the systemknows who the user is, the user's profession, what the user is workingon, the user's professional, social, and personal networks, etc.Additional user data and context information indicates that the user isa parent of a fifteen year old girl whose birthday is in a couple ofweeks. Additional user data and context information can indicate thatthe user is planning to take the user's daughter to take a drivers teston the daughter's birthday and, based on characteristics of a batch ofrecent online searches about how to prepare for a drivers test, a userpriority corresponding to helping to prepare the daughter for herdrivers test can be inferred. As can be appreciated, the user prioritycan be categorized as home/personal related, and can continue as a userpriority even during working hours at a location corresponding to theuser's office (i.e., work/productivity related location).

According to the first example, an indication of a user interaction canbe received by the system 200, such as an interaction associated withopening an application 212 or service 216, or with using the digitalpersonal assistant 214 that is operative or configured to provide ablended work/life user experience. According to an example, theapplication 212, digital personal assistant 214, or service 216 isoperative or configured to display a GUI 300 including information thatis currently relevant to the user. For example, based on the user'sprevious user interactions and patterns and characteristics learned fromthe user's previous interactions and based on current contextinformation, a home/personal-work/productivity context threshold iscalculated for determining user interactions, content items, categoriesof information, events, or other information items that are likely tohave a high degree of relevance to the user in the particular currentcontext. According to an aspect, the information that is relevant to theuser in the current context includes a blend of home/personal andwork/productivity related information, and the personalized displayengine 218 is operative or configured to arrange the information fordisplay to the user in the example GUI 300 based on a determinedrelevance to the user.

According to the example, the GUI 300 can include a variety ofwork/productivity related information, such as general companyinformation, newsfeeds that the user typically reads that are specificto the user's profession and industry, trending searches relating tocurrent popular searches that can be relative and specific to the user,recent documents, information relating to projects that the user isworking on, the user's schedule, unread work/productivity relatedmessages, task items, and other work/productivity related informationitems that are currently relevant to the user. The GUI 300 can furtherinclude a variety of currently relevant home/personal relatedinformation, such as information relating to helping to prepare theuser's daughter for her drivers test, newsfeeds that the user typicallyreads that are specific to the user's hobbies or personal interests,unread home/personal related messages, home/personal related task items,and other home/personal related information that is currently relevantto the user.

According to an aspect, based on a determined degree of relevance to theuser, the personalized display engine 218 allocates particular screenresources to the work/productivity related information and home/personalrelated information. For example, higher priority items that aredetermined to be more relevant to the user can be assigned to primepositions 302, 304, 306 in the GUI 300, wherein a prime position can berelated to a position on the screen that is determined to be a prominentfocal point. For example, a highest ranked work/productivity relatedinformation item or home/personal related information item can bedisplayed in a first prime position 302 associated with a primaryposition in the GUI 300. In some examples, a size of the display of awork/productivity related information or home/personal relatedinformation is associated with the determined relevance or priority tothe user. For example and as illustrated in FIG. 3, a work/productivityrelated information or home/personal related information displayed inthe first prime position 302 is displayed larger than other informationdisplayed in the GUI 300.

According to one example, the first prime position 302 is designated toinclude a work/productivity related and home/personal related blend ofnavigational scopes, intelligent task completion items, and quick linksand actions. According to another example, a second prime position 304is designed to include a blend of company feeds, professional feeds,industry feeds, and personal-related feeds. According to anotherexample, a third prime position 306 is designed to include a blend oftrending company searches and quick links, trending personal-relatedsearches and quick links, project information, most recent documents,general company information, and the like.

According to an aspect, when characteristics of the user's currentcontext change, the arrangement of information in the GUI 300 ispersonalized to the user as determined by the personalized displayengine 218. In a second example, consider that the user's currentcontext indicates a scenario where the user is on vacation. Based on theuser's previous user interactions and patterns and characteristicslearned from the user's previous interactions and based on currentcontext information, a home/personal-work/productivity context thresholdis calculated for determining user interactions, content items,categories of information, events, or other information items that arelikely to have a high degree of relevance to the user in the particularcurrent context. For example, based on the current context, items thatare likely to be relevant to the user can include a higher threshold ofhome/personal information, and/or home/personal information can bearranged in prime positions 302, 304, 306 in the GUI 300.

Having described an operating environment 100, an example system 200,and user interface display examples with respect to FIGS. 1-3, FIGS.4A-B are a flow chart showing general stages involved in an examplemethod 400 for generating a user experience that provides a blendedworkflow of relevant information to the user based on the user's currentwork and life characteristics. According to examples, OPERATIONS 404-410can iterate in a continual loop for learning and understanding userinteraction patterns and home/personal related and work/productivityrelated characteristics, and OPERATIONS 404-406 and 412-420 illustrateproviding the user with a blend of home/personal related andwork/productivity related information based on the user's intent. Withreference now to FIG. 4A, the method 400 begins at start OPERATION 402,and proceeds to OPERATION 404, where the user interaction monitoringengine 204 monitors one or more client computing devices 102 associatedwith a user for a user interaction, such as a search query, launching oraccessing an application 212, accessing, modifying, or copying a file,etc., navigating to a website, downloading, rendering, or playing onlinecontent, or other user interaction. In some examples, a series orsequence of user device interactions can be mapped to an interactionevent, such that the interaction event is detected upon determining thatthe user data indicate the series or sequence of user interactions hasbeen carried out by the user.

The method 400 proceeds to OPERATION 406, where user data andcharacteristics and context information associated with the userinteraction are collected. For example, the data can be collected fromone or more data sources 104, from one or more sensors 108 integratedwith or communicatively attached to one or more computing devices.Examples of user data and sensor data are described above with respectto FIG. 2. At OPERATION 408, the collected or extracted user interactioninformation can be stored in a user profile associated with the user.

The method 400 proceeds from OPERATION 408 to OPERATION 410, where thepattern understanding engine 206 derives user interaction patterns basedon user interaction information determined by the user interactionmonitoring engine 204. According to an aspect, one or more algorithmscan be applied to the user interaction information to determine orunderstand a set of patterns that can be characterized as home/personalrelated or work/productivity related. For example, home/personal relatedor work/productivity related patterns can be determined based on similarinstances of observation of user interactions or associated contextinformation. The method 400 can end by proceeding to OPERATION 498, orcontinue in providing the user with a blend of home/personal related andwork/productivity related information based on the user's intent byproceeding from OPERATION 406 to OPERATION 412 in FIG. 4B.

At OPERATION 412, the pattern understanding engine 206 can use semanticanalysis to identify home/personal related or work/productivity relatedcharacteristics and determine semantic information related to the userinteraction, and compare characteristics of the current user interactionagainst historical user interactions. At OPERATION 414, the patternunderstanding engine 206 infers home/personal interactions andwork/productivity interactions that the user is likely to perform orinformation that is likely to be relevant to the user based onhistorical user interactions that are similar to the current interactionand based on the current context.

The method 400 proceeds to OPERATION 416, where, based on variouscharacteristics associated with the current user interaction and on apattern determined to be the most similar to the current interaction bythe pattern understanding engine 206, the context threshold engine 220determines a home/personal-work/productivity context threshold forcalculating edge weights (in the knowledge graph) representingrelationships between the user and entities associated with the inferredinteractions that the user is likely to perform or information likely tobe relevant to the user in the current context. For example, twoparticular nodes with a strong edge weight between them indicate aninteractivity or information with a high degree of relevance to the userin the current context.

The method 400 proceeds to OPTIONAL OPERATION 418, where thepersonalized display engine 218 allocates screen resources for a blendof home/personal and work/productivity related information based on adetermined priority or relevance of an information item to the user'scurrent context, wherein the relevance is calculated based on thedetermined home/personal-work/productivity context threshold. AtOPERATION 420, a GUI 300 is generated, and personalized content isprovided to the user in the GUI based on the user's intent. The method400 ends at END OPERATION 498.

While implementations have been described in the general context ofprogram modules that execute in conjunction with an application programthat runs on an operating system on a computer, those skilled in the artwill recognize that aspects may also be implemented in combination withother program modules. Generally, program modules include routines,programs, components, data structures, and other types of structuresthat perform particular tasks or implement particular abstract datatypes.

The aspects and functionalities described herein may operate via amultitude of computing systems including, without limitation, desktopcomputer systems, wired and wireless computing systems, mobile computingsystems (e.g., mobile telephones, netbooks, tablet or slate typecomputers, notebook computers, and laptop computers), hand-held devices,multiprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, and mainframe computers.

In addition, according to an aspect, the aspects and functionalitiesdescribed herein operate over distributed systems (e.g., cloud-basedcomputing systems), where application functionality, memory, datastorage and retrieval and various processing functions are operatedremotely from each other over a distributed computing network, such asthe Internet or an intranet. According to an aspect, user interfaces andinformation of various types are displayed via on-board computing devicedisplays or via remote display units associated with one or morecomputing devices. For example, user interfaces and information ofvarious types are displayed and interacted with on a wall surface ontowhich user interfaces and information of various types are projected.Interaction with the multitude of computing systems with whichimplementations are practiced include, keystroke entry, touch screenentry, voice or other audio entry, gesture entry where an associatedcomputing device is equipped with detection (e.g., camera) functionalityfor capturing and interpreting user gestures for controlling thefunctionality of the computing device, and the like.

FIGS. 5-7 and the associated descriptions provide a discussion of avariety of operating environments in which examples are practiced.However, the devices and systems illustrated and discussed with respectto FIGS. 5-7 are for purposes of example and illustration and are notlimiting of a vast number of computing device configurations that areused for practicing aspects, described herein.

FIG. 5 is a block diagram illustrating physical components (i.e.,hardware) of a computing device 500 with which examples of the presentdisclosure may be practiced. In a basic configuration, the computingdevice 500 includes at least one processing unit 502 and a system memory504. According to an aspect, depending on the configuration and type ofcomputing device, the system memory 504 comprises, but is not limitedto, volatile storage (e.g., random access memory), non-volatile storage(e.g., read-only memory), flash memory, or any combination of suchmemories. According to an aspect, the system memory 504 includes anoperating system 505 and one or more program modules 506 suitable forrunning software applications 550. According to an aspect, the systemmemory 504 includes the one or more components of the example system 200(e.g., user data collector 202, user interaction monitoring engine 204,pattern understanding engine 206, context threshold engine 220, blendedcontent client 210, and personalized display engine 218). The operatingsystem 505, for example, is suitable for controlling the operation ofthe computing device 500. Furthermore, aspects are practiced inconjunction with a graphics library, other operating systems, or anyother application program, and is not limited to any particularapplication or system. This basic configuration is illustrated in FIG. 5by those components within a dashed line 508. According to an aspect,the computing device 500 has additional features or functionality. Forexample, according to an aspect, the computing device 500 includesadditional data storage devices (removable and/or non-removable) suchas, for example, magnetic disks, optical disks, or tape. Such additionalstorage is illustrated in FIG. 5 by a removable storage device 509 and anon-removable storage device 510.

As stated above, according to an aspect, a number of program modules anddata files are stored in the system memory 504. While executing on theprocessing unit 502, the program modules 506 (e.g., one or morecomponents of the example system 200 (e.g., user data collector 202,user interaction monitoring engine 204, pattern understanding engine206, context threshold engine 220, blended content client 210, andpersonalized display engine 218) perform processes including, but notlimited to, one or more of the stages of the method 400 illustrated inFIGS. 4A and 4B. According to an aspect, other program modules are usedin accordance with examples and include applications such as electronicmail and contacts applications, word processing applications,spreadsheet applications, database applications, slide presentationapplications, drawing or computer-aided application programs, etc.

According to an aspect, aspects are practiced in an electrical circuitcomprising discrete electronic elements, packaged or integratedelectronic chips containing logic gates, a circuit using amicroprocessor, or on a single chip containing electronic elements ormicroprocessors. For example, aspects are practiced via asystem-on-a-chip (SOC) where each or many of the components illustratedin FIG. 5 are integrated onto a single integrated circuit. According toan aspect, such an SOC device includes one or more processing units,graphics units, communications units, system virtualization units andvarious application functionality all of which are integrated (or“burned”) onto the chip substrate as a single integrated circuit. Whenoperating via an SOC, the functionality, described herein, is operatedvia application-specific logic integrated with other components of thecomputing device 500 on the single integrated circuit (chip). Accordingto an aspect, aspects of the present disclosure are practiced usingother technologies capable of performing logical operations such as, forexample, AND, OR, and NOT, including but not limited to mechanical,optical, fluidic, and quantum technologies. In addition, aspects arepracticed within a general purpose computer or in any other circuits orsystems.

According to an aspect, the computing device 500 has one or more inputdevice(s) 512 such as a keyboard, a mouse, a pen, a sound input device,a touch input device, etc. The output device(s) 514 such as a display,speakers, a printer, etc. are also included according to an aspect. Theaforementioned devices are examples and others may be used. According toan aspect, the computing device 500 includes one or more communicationconnections 516 allowing communications with other computing devices518. Examples of suitable communication connections 516 include, but arenot limited to, radio frequency (RF) transmitter, receiver, and/ortransceiver circuitry; universal serial bus (USB), parallel, and/orserial ports.

The term computer readable media as used herein include computer storagemedia. Computer storage media include volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, or program modules. The system memory504, the removable storage device 509, and the non-removable storagedevice 510 are all computer storage media examples (i.e., memorystorage.) According to an aspect, computer storage media include RAM,ROM, electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other article ofmanufacture which can be used to store information and which can beaccessed by the computing device 500. According to an aspect, any suchcomputer storage media is part of the computing device 500. Computerstorage media do not include a carrier wave or other propagated datasignal.

According to an aspect, communication media are embodied by computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and includes any information delivery medium. According to anaspect, the term “modulated data signal” describes a signal that has oneor more characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency (RF), infrared, and other wireless media.

FIGS. 6A and 6B illustrate a mobile computing device 600, for example, amobile telephone, a smart phone, a tablet personal computer, a laptopcomputer, and the like, with which aspects may be practiced. Withreference to FIG. 6A, an example of a mobile computing device 600 forimplementing the aspects is illustrated. In a basic configuration, themobile computing device 600 is a handheld computer having both inputelements and output elements. The mobile computing device 600 typicallyincludes a display 605 and one or more input buttons 610 that allow theuser to enter information into the mobile computing device 600.According to an aspect, the display 605 of the mobile computing device600 functions as an input device (e.g., a touch screen display). Ifincluded, an optional side input element 615 allows further user input.According to an aspect, the side input element 615 is a rotary switch, abutton, or any other type of manual input element. In alternativeexamples, mobile computing device 600 incorporates more or less inputelements. For example, the display 605 may not be a touch screen in someexamples. In alternative examples, the mobile computing device 600 is aportable phone system, such as a cellular phone. According to an aspect,the mobile computing device 600 includes an optional keypad 635.According to an aspect, the optional keypad 635 is a physical keypad.According to another aspect, the optional keypad 635 is a “soft” keypadgenerated on the touch screen display. In various aspects, the outputelements include the display 605 for showing a graphical user interface(GUI), a visual indicator 620 (e.g., a light emitting diode), and/or anaudio transducer 625 (e.g., a speaker). In some examples, the mobilecomputing device 600 incorporates a vibration transducer for providingthe user with tactile feedback. In yet another example, the mobilecomputing device 600 incorporates input and/or output ports, such as anaudio input (e.g., a microphone jack), an audio output (e.g., aheadphone jack), and a video output (e.g., a HDMI port) for sendingsignals to or receiving signals from an external device. In yet anotherexample, the mobile computing device 600 incorporates peripheral deviceport 640, such as an audio input (e.g., a microphone jack), an audiooutput (e.g., a headphone jack), and a video output (e.g., a HDMI port)for sending signals to or receiving signals from an external device.

FIG. 6B is a block diagram illustrating the architecture of one exampleof a mobile computing device. That is, the mobile computing device 600incorporates a system (i.e., an architecture) 602 to implement someexamples. In one example, the system 602 is implemented as a “smartphone” capable of running one or more applications (e.g., browser,e-mail, calendaring, contact managers, messaging clients, games, andmedia clients/players). In some examples, the system 602 is integratedas a computing device, such as an integrated personal digital assistant(PDA) and wireless phone.

According to an aspect, one or more application programs 650 are loadedinto the memory 662 and run on or in association with the operatingsystem 664. Examples of the application programs include phone dialerprograms, e-mail programs, personal information management (PIM)programs, word processing programs, spreadsheet programs, Internetbrowser programs, messaging programs, and so forth. According to anaspect, one or more components of the example system 200 (e.g., userdata collector 202, user interaction monitoring engine 204, patternunderstanding engine 206, context threshold engine 220, blended contentclient 210, and personalized display engine 218) are loaded into memory662. The system 602 also includes a non-volatile storage area 668 withinthe memory 662. The non-volatile storage area 668 is used to storepersistent information that should not be lost if the system 602 ispowered down. The application programs 650 may use and store informationin the non-volatile storage area 668, such as e-mail or other messagesused by an e-mail application, and the like. A synchronizationapplication (not shown) also resides on the system 602 and is programmedto interact with a corresponding synchronization application resident ona host computer to keep the information stored in the non-volatilestorage area 668 synchronized with corresponding information stored atthe host computer. As should be appreciated, other applications may beloaded into the memory 662 and run on the mobile computing device 600.

According to an aspect, the system 602 has a power supply 670, which isimplemented as one or more batteries. According to an aspect, the powersupply 670 further includes an external power source, such as an ACadapter or a powered docking cradle that supplements or recharges thebatteries.

According to an aspect, the system 602 includes a radio 672 thatperforms the function of transmitting and receiving radio frequencycommunications. The radio 672 facilitates wireless connectivity betweenthe system 602 and the “outside world,” via a communications carrier orservice provider. Transmissions to and from the radio 672 are conductedunder control of the operating system 664. In other words,communications received by the radio 672 may be disseminated to theapplication programs 650 via the operating system 664, and vice versa.

According to an aspect, the visual indicator 620 is used to providevisual notifications and/or an audio interface 674 is used for producingaudible notifications via the audio transducer 625. In the illustratedexample, the visual indicator 620 is a light emitting diode (LED) andthe audio transducer 625 is a speaker. These devices may be directlycoupled to the power supply 670 so that when activated, they remain onfor a duration dictated by the notification mechanism even though theprocessor 660 and other components might shut down for conservingbattery power. The LED may be programmed to remain on indefinitely untilthe user takes action to indicate the powered-on status of the device.The audio interface 674 is used to provide audible signals to andreceive audible signals from the user. For example, in addition to beingcoupled to the audio transducer 625, the audio interface 674 may also becoupled to a microphone to receive audible input, such as to facilitatea telephone conversation. According to an aspect, the system 602 furtherincludes a video interface 676 that enables an operation of an on-boardcamera 630 to record still images, video stream, and the like.

According to an aspect, a mobile computing device 600 implementing thesystem 602 has additional features or functionality. For example, themobile computing device 600 includes additional data storage devices(removable and/or non-removable) such as, magnetic disks, optical disks,or tape. Such additional storage is illustrated in FIG. 6B by thenon-volatile storage area 668.

According to an aspect, data/information generated or captured by themobile computing device 600 and stored via the system 602 is storedlocally on the mobile computing device 600, as described above.According to another aspect, the data is stored on any number of storagemedia that are accessible by the device via the radio 672 or via a wiredconnection between the mobile computing device 600 and a separatecomputing device associated with the mobile computing device 600, forexample, a server computer in a distributed computing network, such asthe Internet. As should be appreciated such data/information isaccessible via the mobile computing device 600 via the radio 672 or viaa distributed computing network. Similarly, according to an aspect, suchdata/information is readily transferred between computing devices forstorage and use according to well-known data/information transfer andstorage means, including electronic mail and collaborativedata/information sharing systems.

FIG. 7 illustrates one example of the architecture of a system forproviding an efficient blend of home/personal and work/productivityrelated content based on a user's intent as described above. Contentdeveloped, interacted with, or edited in association with the one ormore components of the example system 200 (e.g., user data collector202, user interaction monitoring engine 204, pattern understandingengine 206, context threshold engine 220, blended content client 210,and personalized display engine 218) is enabled to be stored indifferent communication channels or other storage types. For example,various documents may be stored using a directory service 722, a webportal 724, a mailbox service 726, an instant messaging store 728, or asocial networking site 730. One or more components of the example system200 (e.g., user data collector 202, user interaction monitoring engine204, pattern understanding engine 206, context threshold engine 220,blended content client 210, and personalized display engine 218) areoperative or configured to use any of these types of systems or the likefor providing an efficient blend of home/personal and work/productivityrelated content based on a user's intent, as described herein. Accordingto an aspect, a server 720 provides the one or more components of theexample system 200 (e.g., user data collector 202, user interactionmonitoring engine 204, pattern understanding engine 206, contextthreshold engine 220, blended content client 210, and personalizeddisplay engine 218) to clients 705 a,b,c. As one example, the server 720is a web server providing one or more components of the example system200 over the web. The server 720 provides one or more components of theexample system 200 over the web to clients 705 through a network 740. Byway of example, the client computing device is implemented and embodiedin a personal computer 705 a, a tablet computing device 705 b or amobile computing device 705 c (e.g., a smart phone), or other computingdevice. Any of these examples of the client computing device areoperable to obtain content from the store 716.

Implementations, for example, are described above with reference toblock diagrams and/or operational illustrations of methods, systems, andcomputer program products according to aspects. The functions/acts notedin the blocks may occur out of the order as shown in any flowchart. Forexample, two blocks shown in succession may in fact be executedsubstantially concurrently or the blocks may sometimes be executed inthe reverse order, depending upon the functionality/acts involved.

The description and illustration of one or more examples provided inthis application are not intended to limit or restrict the scope asclaimed in any way. The aspects, examples, and details provided in thisapplication are considered sufficient to convey possession and enableothers to make and use the best mode. Implementations should not beconstrued as being limited to any aspect, example, or detail provided inthis application. Regardless of whether shown and described incombination or separately, the various features (both structural andmethodological) are intended to be selectively included or omitted toproduce an example with a particular set of features. Having beenprovided with the description and illustration of the presentapplication, one skilled in the art may envision variations,modifications, and alternate examples falling within the spirit of thebroader aspects of the general inventive concept embodied in thisapplication that do not depart from the broader scope.

We claim:
 1. A system for providing a personalized blend of relevantcontent to a user based on an inferred context, the system comprising:at least one processing device; and at least one computer readable datastorage device storing instructions that, when executed by the at leastone processing device, cause the system to: receive an indication of acurrent user interaction on a computing device; determine ahome/personal-work/productivity context threshold for the current userinteraction based on historic user interaction patterns; and selectivelydisplay a blend of home/personal and work/productivity information basedon a determined context threshold.
 2. The system of claim 1, wherein thecurrent user interaction comprises: launching or accessing anapplication or service operative to provide personalized content to theuser; or a search query.
 3. The system of claim 1, wherein prior toreceiving the indication of the current user interaction, the system isoperative to: receive indications of user interactions; collect userdata and interaction information associated with the user interactions;and determine and store a plurality of user interaction patterns basedon the user data and user interaction information.
 4. The system ofclaim 3, wherein in determining the plurality of user interactionpatterns, the system is operative to categorize characteristics of eachof the plurality of user interaction patterns as home/personal relatedor work/productivity related.
 5. The system of claim 3, wherein the userdata and the user interaction information comprise data sensed ordetermined from one or more sensors integrated in or communicativelyattached to the computing device.
 6. The system of claim 5, wherein thesystem is further operative to collect the user data and the userinteraction information associated with the current user interaction. 7.The system of claim 6, wherein in determining thehome/personal-work/productivity context threshold for the current userinteraction, the system is operative to: identify at least onehistorical user interaction that is similar to a current interactionbased in part on the user data and the user interaction information; andinfer interactions that the user is likely to perform or informationthat is likely to be relevant to the user in a current context of thecurrent interaction; and determine a ratio of home/personal informationto work/productivity related information to surface to the user.
 8. Thesystem of claim 7, wherein the system is further operative to rank theinferred interactions and information based on context informationassociated with the current interaction, wherein a higher edge weightindicates a higher degree of relevance to the user.
 9. The system ofclaim 8, wherein screen positions at which inferred interactions andinformation are displayed are allocated based on a degree of relevanceto the user.
 10. The system of claim 1, wherein in selectivelydisplaying the blend of home/personal and work/productivity information,the system is operative to selectively display the blend ofhome/personal and work/productivity workflows, the workflows comprisingwork/productivity and home/personal related tasks.
 11. Acomputer-implemented method for providing a personalized blend ofrelevant content to a user based on an inferred context, comprising:receiving an indication of a user interaction on a computing device;determining a home/personal-work/productivity context threshold for theuser interaction based on historic user interaction patterns; andselectively displaying a blend of home/personal and work/productivityinformation based on the determined home/personal-work/productivitycontext threshold.
 12. The method of claim 11, wherein selectivelydisplaying the blend of home/personal and work/productivity informationcomprises selectively displaying the blend of home/personal andwork/productivity workflows, the workflows comprising work/productivityand home/personal related tasks.
 13. The method of claim 11, whereinreceiving the indication of the user interaction comprises receiving theindication of one of: launching or accessing an application, service, ora digital personal assistant operative to provide personalized contentto the user; or a search query.
 14. The method of claim 11, whereinprior to determining the home/personal-work/productivity contextthreshold for the user interaction: collecting user data and interactioninformation associated with the user interaction and with past userinteractions; and determining a plurality of user interaction patternsbased on user data and user interaction information.
 15. The method ofclaim 14, wherein determining the plurality of user interaction patternscomprises categorizing characteristics of each of the plurality of userinteraction patterns as home/personal related or work/productivityrelated.
 16. The method of claim 11, wherein determining ahome/personal-work/productivity context threshold for the userinteraction comprises: identifying at least one historical userinteraction that is similar to a current interaction; and inferringinteractions that the user is likely to perform or information that islikely to be relevant to the user in a current context of the currentinteraction; and determining a ratio of home/personal information towork/productivity related information to surface to the user.
 17. Themethod of claim 16, further comprising: using thehome/personal-work/productivity context threshold to calculate weightsof edges in a knowledge graph representing relationships between theuser and entities associated with the inferred interactions that theuser is likely to perform or information likely to be relevant to theuser in the current context; and ranking the inferred interactions andinformation based on calculated edge weights, wherein a higher edgeweight indicates a higher degree of relevance to the user.
 18. Themethod of claim 17, wherein selectively displaying the blend ofhome/personal and work/productivity information comprises allocatingscreen positions at which the inferred interactions and information aredisplayed based on a degree of relevance to the user.
 19. The method ofclaim 18, wherein allocating screen resources comprises assigning primescreen positions to information that is determined to be more relevantto the user.
 20. A computer readable storage device including computerreadable instructions, which when executed by a processing unit isoperative to: collect user data and interaction information associatedwith user interactions; determine and store a plurality of userinteraction patterns based on the user data and user interactioninformation; receive an indication of a current user interaction on acomputing device related to a user; determine ahome/personal-work/productivity context threshold for the current userinteraction based on historic user interaction patterns; and selectivelydisplay a blend of home/personal and work/productivity information basedon a determined context threshold, wherein in selectively displaying theblend of home/personal and work/productivity information, the computerreadable storage device is operative to allocate screen resources toinferred interactions and information based on a degree of relevance tothe user determined by the determined context threshold, wherein screenpositions are allocated based on a determined degree of relevance to theuser.